盲欠定混合物识别中高阶累积量阵列的典型分解

A. Karfoul, L. Albera, L. De Lathauwer
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引用次数: 12

摘要

本文提出了一种新的方法,称为2q-ORBIT (q > 1),用于盲目识别统计独立源的潜在欠确定混合物。这些方法是基于q阶累积量的典型分解。后一种分解可归结为一个加载矩阵是酉的三阶数组的分解。然后通过交替和重复两个方案来计算这种分解,直到收敛:第一个方案包括解决Procrustes问题,而第二个方案需要计算几个q阶数组的最佳秩1近似。计算机结果表明,与传统的基于累积量的算法相比,本文提出的方法具有较好的效率,特别是在欠确定情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Canonical decomposition of even higher order cumulant arrays for blind underdetermined mixture identification
A new family of methods, named 2q-ORBIT (q > 1), is proposed in this paper in order to blindly identify potentially underdetermined mixtures of statistically independent sources. These methods are based on the canonical decomposition of q-th order (q ges 2) cumulants. The latter decomposition is brought back to the decomposition of a third order array whose one loading matrix is unitary. Such a decomposition is then computed by alterning and repeating two schemes until convergence: the first one consists in solving a Procrustes problem while the second one needs to compute the best rank-1 approximation of several q-th order arrays. Computer results show a good efficiency of the proposed methods with respect to classical cumulant-based algorithms especially in the underdetermined case.
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